





import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase				(		_lowerCamelCase  ):
      def __init__(		self   ,							*a__   ,							**a__							):
         super().__init__(*a__   ,							**a__							)
         A_  :					Dict         =						{}
      def 		_lowerCamelCase   (		self   ,							a__   ,							*a__   ,							**a__							):
         A_  :					Any         =						super().add_tokens(a__   ,							*a__   ,							**a__							)
         if num_added_tokens == 0:
            raise ValueError(
                F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                """ `placeholder_token` that is not already in the tokenizer."""							)
      def 		_lowerCamelCase   (		self   ,							a__   ,							*a__   ,							a__=1   ,							**a__							):
         A_  :					Any         =						[]
         if num_vec_per_token == 1:
            self.try_adding_tokens(a__   ,							*a__   ,							**a__							)
            output.append(a__							)
         else:
            A_  :					Tuple         =						[]
            for i in range(a__							):
               A_  :					Any         =						placeholder_token + F"""_{i}"""
               self.try_adding_tokens(a__   ,							*a__   ,							**a__							)
               output.append(a__							)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   F"""The tokenizer already has placeholder token {token} that can get confused with"""
                   F""" {placeholder_token}keep placeholder tokens independent"""							)
         A_  :					List[str]         =						output
      def 		_lowerCamelCase   (		self   ,							a__   ,							a__=False   ,							a__=1.0							):
         if isinstance(a__   ,							a__							):
            A_  :					Tuple         =						[]
            for i in range(len(a__							)							):
               output.append(self.replace_placeholder_tokens_in_text(text[i]   ,							vector_shuffle=a__							)							)
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               A_  :					Union[str, Any]         =						self.token_map[placeholder_token]
               A_  :					Union[str, Any]         =						tokens[: 1 + int(len(a__							) * prop_tokens_to_load							)]
               if vector_shuffle:
                  A_  :					int         =						copy.copy(a__							)
                  random.shuffle(a__							)
               A_  :					Union[str, Any]         =						text.replace(a__   ,							""" """.join(a__							)							)
         return text
      def __call__(		self   ,							a__   ,							*a__   ,							a__=False   ,							a__=1.0   ,							**a__							):
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 a__   ,							vector_shuffle=a__   ,							prop_tokens_to_load=a__							)   ,							*a__   ,							**a__   ,							)






      def 		_lowerCamelCase   (		self   ,							a__   ,							*a__   ,							a__=False   ,							a__=1.0   ,							**a__							):
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 a__   ,							vector_shuffle=a__   ,							prop_tokens_to_load=a__							)   ,							*a__   ,							**a__   ,							)














"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)